Department of Mechanical Engineering, University of California Merced, Merced, CA 95343, USA.
Sensors (Basel). 2022 Apr 11;22(8):2913. doi: 10.3390/s22082913.
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is one of the most important and challenging problems in the area of intelligent sensor fault diagnostics. Within this frame of reference, we extended the physics-informed transfer learning framework, first presented previously for a fault cause assignment, to the level of sensor fault diagnostics for a range of different fault scenarios. Hence, the framework is utilized to perform intelligent sensor fault diagnostics for the first time. The underlying dynamics of the reference system are extracted using a completely data-driven methodology and dynamic mode decomposition with control (DMDc) in order to generate time-frequency illustrations of each sample with continuous wavelet transform (CWT). Then, sensor fault diagnostics for bias, drift over time, sine disturbance and increased noise sensor fault scenarios are achieved using the idea of transfer learning with a pre-trained image classification algorithm. The classification results yields a good performance on sensor fault diagnostics with 91.5% training and 84.7% test accuracy along with a fair robustness level with a set of reference benchmark system parameters.
智能健康监测、智能故障检测和诊断领域正在迅速扩展，以维持许多工程应用中的成功运行。考虑到系统中可能发生的故障情况，指示传感器中的故障类型是智能传感器故障诊断领域中最重要和最具挑战性的问题之一。在这个参考框架内，我们扩展了物理信息迁移学习框架，该框架之前首先用于故障原因分配，扩展到了一系列不同故障情况的传感器故障诊断级别。因此，该框架首次用于执行智能传感器故障诊断。使用完全基于数据的方法和带控制的动态模态分解（DMDc）提取参考系统的基础动力学，以便使用连续小波变换（CWT）为每个样本生成时频说明。然后，使用预训练的图像分类算法的迁移学习思想，实现了偏置、随时间漂移、正弦干扰和增加噪声传感器故障情况的传感器故障诊断。分类结果在传感器故障诊断方面表现出良好的性能，训练准确率为 91.5%，测试准确率为 84.7%，并且具有一定的稳健性水平，参考基准系统参数集。
